COVIXNet: A Robust and Explainable Deep Transfer Learning Framework for COVID-19 Detection from Chest X-ray Imagery

The COVID-19 pandemic has highlighted the urgent need for fast, accurate, and trustworthy diagnostic tools to complement routine testing procedures. While Chest X-ray (CXR) imaging is a valuable alternative, it requires specialized interpretation that is prone to subjectivity, creating a bottleneck in clinical workflows. This study introduces COVIXNet, a robust and explainable deep transfer learning framework designed to automate the detection of COVID-19 from CXR images with high accuracy and provide clinically interpretable justifications for its predictions. COVIXNet is built upon a DenseNet-121 backbone, enhanced with a spatial attention mechanism to focus on diagnostically significant lung regions. Explainability is achieved using Gradient-weighted Class Activation Mapping (Grad-CAM). The model was trained and evaluated on COVIDXSet, a curated multi-source dataset of 8,591 CXR images. A two-phase transfer learning strategy was employed for effective feature adaptation. COVIXNet demonstrated state-of-the-art performance, achieving an accuracy of 96.8% (95% CI: 95.9% - 97.7%), a precision of 97.2%, and an AUC-ROC of 0.993 (95% CI: 0.989 - 0.997). The explainability module generated clinically meaningful heatmaps, with 92% rated as relevant by expert radiologists and achieving a high Pointing Game Accuracy of 81.5%. The model also showed consistent performance across demographic subgroups and varying image quality. COVIXNet offers a powerful combination of high diagnostic accuracy, robustness, and validated explainability. With a 48 ms inference time and 33.2 MB model size, COVIXNet is a promising and efficient tool for deployment in clinical settings to assist healthcare professionals in the rapid triage and diagnosis of COVID-19.